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ATD: Surveillance Evasion and Threat Avoidance

ATD：监视规避和威胁规避

基本信息

批准号：
1738010
负责人：
Alexander Vladimirsky
金额：
$ 40万
依托单位：
Cornell University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1738010&HistoricalAwards=false
关键词：
ATD Surveillance Evasion Threat Avoidance

项目摘要

The growing availability of data on pedestrian movement allows for increasingly sophisticated models of people's goals, preferences, and perception of their environment. Such models are important not only in traffic engineering ("How do we structure the foyer in this building to avoid a stampede in case of evacuation?") but also in improving our ability to detect emerging threats in urban settings. Any dramatic change in the usual "patterns of life" might provide a clue about the evolving conditions ("Why did this group of pedestrians take such an unusual path to their target? Why is there suddenly a crowd at this intersection?"), while the analysis of data aggregated over a longer horizon can be also useful in improving our monitoring and modeling ("Which parts of the city are generally perceived as more dangerous? Where should we deploy our limited observation resources?"). This project focuses on two specific applications: (a) civilians in dangerous environments planning their paths to minimize threat exposure, and (b) adversaries aware of the existing monitoring measures planning their paths to evade the observation. In both contexts, the PIs propose models and numerical methods for (1) path planning based on one's beliefs about the environment and (2) adversarial/robust path planning, with the environment possibly changing in response to people's routing choices. The threat avoidance under selfish/independent decision making will be also treated in the framework of "mean field games". The proposed approach draws on methods from game theory, convex optimization, optimal control, and multi-objective dynamic programming. The information patterns built into this model make it possible to leverage the efficiency of fast numerical algorithms already developed for a broad range of deterministic optimal control applications. This project continues earlier work on causal/non-iterative numerical methods and distributed optimal control.

越来越多的行人运动数据的可用性使得人们的目标、偏好和对环境的感知的模型变得越来越复杂。这些模型不仅在交通工程中很重要（“我们该如何设计这栋大楼的门厅，以避免疏散时发生踩踏？”）同时也提高了我们在城市环境中发现新威胁的能力。通常的“生活模式”的任何戏剧性变化都可能为不断变化的环境提供线索（“为什么这群行人要走这样一条不寻常的路去他们的目标？”）为什么这个十字路口突然有一群人？)，而对较长时期内汇总的数据进行分析，也有助于改进我们的监测和建模(“城市的哪些部分通常被认为更危险？”我们应该把有限的观察资源部署在哪里？”)。该项目侧重于两个具体应用：(a)危险环境中的平民规划其路径以最大限度地减少威胁暴露，以及(b)了解现有监测措施的对手规划其路径以逃避观察。在这两种情况下，pi提出了(1)基于人们对环境的信念的路径规划和(2)对抗性/鲁棒性路径规划的模型和数值方法，环境可能会随着人们的路径选择而变化。自私/独立决策下的威胁规避也将在“平均场博弈”的框架下进行研究。该方法借鉴了博弈论、凸优化、最优控制和多目标动态规划等方法。该模型内建的信息模式使得利用已经为广泛的确定性最优控制应用开发的快速数值算法的效率成为可能。该项目继续早期在因果/非迭代数值方法和分布式最优控制方面的工作。